Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1904
Missing cells1041
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory253.0 KiB
Average record size in memory136.1 B

Variable types

Text2
Numeric10
Categorical5

Alerts

Status has constant value "Released" Constant
Budget is highly overall correlated with India Gross and 1 other fieldsHigh correlation
Dubbed Languages is highly overall correlated with Industry and 1 other fieldsHigh correlation
India Gross is highly overall correlated with Budget and 2 other fieldsHigh correlation
Industry is highly overall correlated with Dubbed Languages and 1 other fieldsHigh correlation
Original Languages is highly overall correlated with Dubbed Languages and 1 other fieldsHigh correlation
Overseas is highly overall correlated with India Gross and 1 other fieldsHigh correlation
Worldwide is highly overall correlated with Budget and 2 other fieldsHigh correlation
Worldwide has 125 (6.6%) missing values Missing
India Gross has 63 (3.3%) missing values Missing
Overseas has 853 (44.8%) missing values Missing
Worldwide has 46 (2.4%) zeros Zeros
India Telugu Net has 1192 (62.6%) zeros Zeros
India Gross has 57 (3.0%) zeros Zeros
Overseas has 362 (19.0%) zeros Zeros
India Malayalam Net has 1557 (81.8%) zeros Zeros
India Hindi Net has 1501 (78.8%) zeros Zeros
India Gujarati Net has 1797 (94.4%) zeros Zeros
India Marathi Net has 1759 (92.4%) zeros Zeros
India Bengali Net has 1774 (93.2%) zeros Zeros

Reproduction

Analysis started2024-11-24 11:44:32.457091
Analysis finished2024-11-24 11:45:04.668724
Duration32.21 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Distinct408
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:05.484364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length11
Median length6
Mean length6.7694328
Min length6

Characters and Unicode

Total characters12889
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)5.9%

Sample

1st row25 Dec
2nd row13 Mar
3rd row13 Mar
4th row13 Mar
5th row13 Mar
ValueCountFrequency (%)
mar 228
 
5.6%
2024 215
 
5.2%
feb 195
 
4.8%
oct 180
 
4.4%
aug 180
 
4.4%
jan 166
 
4.0%
sep 162
 
4.0%
nov 159
 
3.9%
jun 155
 
3.8%
may 145
 
3.5%
Other values (35) 2316
56.5%
2024-11-24T11:45:06.563396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2197
17.0%
2 1495
 
11.6%
0 1015
 
7.9%
1 877
 
6.8%
a 539
 
4.2%
u 466
 
3.6%
J 452
 
3.5%
e 420
 
3.3%
4 395
 
3.1%
M 373
 
2.9%
Other values (23) 4660
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2197
17.0%
2 1495
 
11.6%
0 1015
 
7.9%
1 877
 
6.8%
a 539
 
4.2%
u 466
 
3.6%
J 452
 
3.5%
e 420
 
3.3%
4 395
 
3.1%
M 373
 
2.9%
Other values (23) 4660
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2197
17.0%
2 1495
 
11.6%
0 1015
 
7.9%
1 877
 
6.8%
a 539
 
4.2%
u 466
 
3.6%
J 452
 
3.5%
e 420
 
3.3%
4 395
 
3.1%
M 373
 
2.9%
Other values (23) 4660
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2197
17.0%
2 1495
 
11.6%
0 1015
 
7.9%
1 877
 
6.8%
a 539
 
4.2%
u 466
 
3.6%
J 452
 
3.5%
e 420
 
3.3%
4 395
 
3.1%
M 373
 
2.9%
Other values (23) 4660
36.2%

Movie
Text

Distinct1755
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:07.333598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length59
Median length44
Mean length13.62395
Min length1

Characters and Unicode

Total characters25940
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1633 ?
Unique (%)85.8%

Sample

1st rowSolo Brathuke So Better
2nd rowShivan
3rd rowEureka
4th rowMadha
5th rowArjuna
ValueCountFrequency (%)
the 111
 
2.7%
of 51
 
1.2%
51
 
1.2%
2 49
 
1.2%
love 26
 
0.6%
2024 23
 
0.6%
a 20
 
0.5%
part 20
 
0.5%
1 20
 
0.5%
and 16
 
0.4%
Other values (2784) 3779
90.7%
2024-11-24T11:45:08.255163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4053
15.6%
2263
 
8.7%
e 1463
 
5.6%
r 1393
 
5.4%
i 1387
 
5.3%
n 1282
 
4.9%
h 1250
 
4.8%
o 967
 
3.7%
t 940
 
3.6%
u 854
 
3.3%
Other values (68) 10088
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4053
15.6%
2263
 
8.7%
e 1463
 
5.6%
r 1393
 
5.4%
i 1387
 
5.3%
n 1282
 
4.9%
h 1250
 
4.8%
o 967
 
3.7%
t 940
 
3.6%
u 854
 
3.3%
Other values (68) 10088
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4053
15.6%
2263
 
8.7%
e 1463
 
5.6%
r 1393
 
5.4%
i 1387
 
5.3%
n 1282
 
4.9%
h 1250
 
4.8%
o 967
 
3.7%
t 940
 
3.6%
u 854
 
3.3%
Other values (68) 10088
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4053
15.6%
2263
 
8.7%
e 1463
 
5.6%
r 1393
 
5.4%
i 1387
 
5.3%
n 1282
 
4.9%
h 1250
 
4.8%
o 967
 
3.7%
t 940
 
3.6%
u 854
 
3.3%
Other values (68) 10088
38.9%

Worldwide
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct713
Distinct (%)40.1%
Missing125
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean215.11103
Minimum0
Maximum17380
Zeros46
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:08.611916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.08
median0.76
Q327.83
95-th percentile488.36
Maximum17380
Range17380
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation1237.885
Coefficient of variation (CV)5.7546329
Kurtosis104.01415
Mean215.11103
Median Absolute Deviation (MAD)0.75
Skewness9.4621498
Sum382682.53
Variance1532359.4
MonotonicityNot monotonic
2024-11-24T11:45:08.950471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 59
 
3.1%
0.04 57
 
3.0%
0.01 53
 
2.8%
0.02 47
 
2.5%
0 46
 
2.4%
0.06 44
 
2.3%
0.05 40
 
2.1%
0.1 34
 
1.8%
0.07 34
 
1.8%
0.09 33
 
1.7%
Other values (703) 1332
70.0%
(Missing) 125
 
6.6%
ValueCountFrequency (%)
0 46
2.4%
0.0001 3
 
0.2%
0.0003 1
 
0.1%
0.001 6
 
0.3%
0.002 3
 
0.2%
0.003 6
 
0.3%
0.004 4
 
0.2%
0.005 4
 
0.2%
0.006 1
 
0.1%
0.007 2
 
0.1%
ValueCountFrequency (%)
17380 3
0.2%
15700 1
 
0.1%
12771 1
 
0.1%
12250 2
0.1%
11180 2
0.1%
8400 2
0.1%
8000 2
0.1%
6634 2
0.1%
6550 1
 
0.1%
6295 2
0.1%

India Telugu Net
Real number (ℝ)

Zeros 

Distinct389
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6124742
Minimum0
Maximum431.01
Zeros1192
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:09.299271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.16
95-th percentile14.5635
Maximum431.01
Range431.01
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation19.44416
Coefficient of variation (CV)5.3825049
Kurtosis179.55573
Mean3.6124742
Median Absolute Deviation (MAD)0
Skewness11.445513
Sum6878.1508
Variance378.07535
MonotonicityNot monotonic
2024-11-24T11:45:09.618474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1192
62.6%
0.03 26
 
1.4%
0.07 22
 
1.2%
0.04 19
 
1.0%
0.06 19
 
1.0%
0.05 19
 
1.0%
0.01 17
 
0.9%
0.02 15
 
0.8%
0.09 14
 
0.7%
0.1 12
 
0.6%
Other values (379) 549
28.8%
ValueCountFrequency (%)
0 1192
62.6%
0.0001 1
 
0.1%
0.001 2
 
0.1%
0.0017 1
 
0.1%
0.002 1
 
0.1%
0.003 5
 
0.3%
0.004 1
 
0.1%
0.005 5
 
0.3%
0.007 3
 
0.2%
0.008 2
 
0.1%
ValueCountFrequency (%)
431.01 1
0.1%
286.78 1
0.1%
220.88 1
0.1%
218.3 1
0.1%
200.98 1
0.1%
169.55 1
0.1%
159.68 1
0.1%
145.48 1
0.1%
137.95 1
0.1%
136.43 1
0.1%

India Gross
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct739
Distinct (%)40.1%
Missing63
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean32.404388
Minimum0
Maximum1416.9
Zeros57
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:09.906499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.08
median0.78
Q318.35
95-th percentile158.7
Maximum1416.9
Range1416.9
Interquartile range (IQR)18.27

Descriptive statistics

Standard deviation99.762601
Coefficient of variation (CV)3.0786757
Kurtosis55.339167
Mean32.404388
Median Absolute Deviation (MAD)0.772
Skewness6.4897423
Sum59656.478
Variance9952.5765
MonotonicityNot monotonic
2024-11-24T11:45:10.267313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 59
 
3.1%
0.03 58
 
3.0%
0 57
 
3.0%
0.01 53
 
2.8%
0.02 47
 
2.5%
0.06 44
 
2.3%
0.05 40
 
2.1%
0.09 34
 
1.8%
0.1 33
 
1.7%
0.07 33
 
1.7%
Other values (729) 1383
72.6%
(Missing) 63
 
3.3%
ValueCountFrequency (%)
0 57
3.0%
0.0001 3
 
0.2%
0.0003 1
 
0.1%
0.001 6
 
0.3%
0.002 3
 
0.2%
0.003 7
 
0.4%
0.004 3
 
0.2%
0.005 4
 
0.2%
0.006 1
 
0.1%
0.007 2
 
0.1%
ValueCountFrequency (%)
1416.9 1
 
0.1%
1000.85 3
0.2%
915.85 3
0.2%
767.25 3
0.2%
760 1
 
0.1%
713.15 1
 
0.1%
660 2
0.1%
657.5 1
 
0.1%
551.5 1
 
0.1%
487.75 2
0.1%

Overseas
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct310
Distinct (%)29.5%
Missing853
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean227.50136
Minimum0
Maximum16902.5
Zeros362
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:10.593962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q316.5
95-th percentile670
Maximum16902.5
Range16902.5
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation1275.5578
Coefficient of variation (CV)5.6068139
Kurtosis102.14546
Mean227.50136
Median Absolute Deviation (MAD)1.5
Skewness9.2832429
Sum239103.93
Variance1627047.7
MonotonicityNot monotonic
2024-11-24T11:45:10.941410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 362
19.0%
0.5 19
 
1.0%
0.2 15
 
0.8%
1 14
 
0.7%
1.5 13
 
0.7%
0.3 11
 
0.6%
2.5 10
 
0.5%
2 10
 
0.5%
3.5 10
 
0.5%
5 10
 
0.5%
Other values (300) 577
30.3%
(Missing) 853
44.8%
ValueCountFrequency (%)
0 362
19.0%
0.01 7
 
0.4%
0.02 3
 
0.2%
0.027 1
 
0.1%
0.03 1
 
0.1%
0.04 2
 
0.1%
0.05 5
 
0.3%
0.07 1
 
0.1%
0.08 2
 
0.1%
0.1 5
 
0.3%
ValueCountFrequency (%)
16902.5 3
0.2%
12199 2
0.1%
9010 1
 
0.1%
7585 1
 
0.1%
6546.76 2
0.1%
5880 2
0.1%
5200 2
0.1%
4600 1
 
0.1%
4500 2
0.1%
3800 1
 
0.1%

Budget
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.758009
Minimum1
Maximum3200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:11.267308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.625
95-th percentile100
Maximum3200
Range3199
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation196.93549
Coefficient of variation (CV)5.6659023
Kurtosis131.21454
Mean34.758009
Median Absolute Deviation (MAD)0
Skewness10.374103
Sum66179.25
Variance38783.586
MonotonicityNot monotonic
2024-11-24T11:45:11.729392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1427
74.9%
15 37
 
1.9%
2 34
 
1.8%
10 31
 
1.6%
5 27
 
1.4%
30 25
 
1.3%
25 22
 
1.2%
20 19
 
1.0%
3 16
 
0.8%
35 15
 
0.8%
Other values (74) 251
 
13.2%
ValueCountFrequency (%)
1 1427
74.9%
1.5 1
 
0.1%
2 34
 
1.8%
2.5 1
 
0.1%
3 16
 
0.8%
3.25 1
 
0.1%
4 13
 
0.7%
5 27
 
1.4%
5.5 1
 
0.1%
6 9
 
0.5%
ValueCountFrequency (%)
3200 3
0.2%
1900 2
 
0.1%
1850 1
 
0.1%
1500 6
0.3%
1300 3
0.2%
1250 2
 
0.1%
1230 1
 
0.1%
1150 1
 
0.1%
900 4
0.2%
820 1
 
0.1%

Verdict
Categorical

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Unknown
1250 
Disaster
289 
Flop
 
80
Hit
 
78
Blockbuster
 
72
Other values (5)
135 

Length

Max length20
Median length7
Mean length7.2295168
Min length3

Characters and Unicode

Total characters13765
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHit
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 1250
65.7%
Disaster 289
 
15.2%
Flop 80
 
4.2%
Hit 78
 
4.1%
Blockbuster 72
 
3.8%
SuperHit 50
 
2.6%
Average 41
 
2.2%
Below Average 16
 
0.8%
Above Average 14
 
0.7%
All Time Blockbuster 14
 
0.7%

Length

2024-11-24T11:45:12.249869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-24T11:45:12.774139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown 1250
63.7%
disaster 289
 
14.7%
blockbuster 86
 
4.4%
flop 80
 
4.1%
hit 78
 
4.0%
average 71
 
3.6%
superhit 50
 
2.5%
below 16
 
0.8%
above 14
 
0.7%
all 14
 
0.7%

Most occurring characters

ValueCountFrequency (%)
n 3750
27.2%
o 1446
 
10.5%
k 1336
 
9.7%
w 1266
 
9.2%
U 1250
 
9.1%
s 664
 
4.8%
e 611
 
4.4%
t 503
 
3.7%
r 496
 
3.6%
i 431
 
3.1%
Other values (17) 2012
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 3750
27.2%
o 1446
 
10.5%
k 1336
 
9.7%
w 1266
 
9.2%
U 1250
 
9.1%
s 664
 
4.8%
e 611
 
4.4%
t 503
 
3.7%
r 496
 
3.6%
i 431
 
3.1%
Other values (17) 2012
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 3750
27.2%
o 1446
 
10.5%
k 1336
 
9.7%
w 1266
 
9.2%
U 1250
 
9.1%
s 664
 
4.8%
e 611
 
4.4%
t 503
 
3.7%
r 496
 
3.6%
i 431
 
3.1%
Other values (17) 2012
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 3750
27.2%
o 1446
 
10.5%
k 1336
 
9.7%
w 1266
 
9.2%
U 1250
 
9.1%
s 664
 
4.8%
e 611
 
4.4%
t 503
 
3.7%
r 496
 
3.6%
i 431
 
3.1%
Other values (17) 2012
14.6%

Industry
Categorical

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Tollywood
726 
Bollywood
414 
Mollywood
355 
Bengali
151 
Marathi
148 

Length

Max length9
Median length9
Mean length8.6859244
Min length7

Characters and Unicode

Total characters16538
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTollywood
2nd rowTollywood
3rd rowTollywood
4th rowTollywood
5th rowTollywood

Common Values

ValueCountFrequency (%)
Tollywood 726
38.1%
Bollywood 414
21.7%
Mollywood 355
18.6%
Bengali 151
 
7.9%
Marathi 148
 
7.8%
Gollywood 110
 
5.8%

Length

2024-11-24T11:45:13.280769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-24T11:45:13.771116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
tollywood 726
38.1%
bollywood 414
21.7%
mollywood 355
18.6%
bengali 151
 
7.9%
marathi 148
 
7.8%
gollywood 110
 
5.8%

Most occurring characters

ValueCountFrequency (%)
o 4815
29.1%
l 3361
20.3%
y 1605
 
9.7%
w 1605
 
9.7%
d 1605
 
9.7%
T 726
 
4.4%
B 565
 
3.4%
M 503
 
3.0%
a 447
 
2.7%
i 299
 
1.8%
Other values (7) 1007
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4815
29.1%
l 3361
20.3%
y 1605
 
9.7%
w 1605
 
9.7%
d 1605
 
9.7%
T 726
 
4.4%
B 565
 
3.4%
M 503
 
3.0%
a 447
 
2.7%
i 299
 
1.8%
Other values (7) 1007
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4815
29.1%
l 3361
20.3%
y 1605
 
9.7%
w 1605
 
9.7%
d 1605
 
9.7%
T 726
 
4.4%
B 565
 
3.4%
M 503
 
3.0%
a 447
 
2.7%
i 299
 
1.8%
Other values (7) 1007
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4815
29.1%
l 3361
20.3%
y 1605
 
9.7%
w 1605
 
9.7%
d 1605
 
9.7%
T 726
 
4.4%
B 565
 
3.4%
M 503
 
3.0%
a 447
 
2.7%
i 299
 
1.8%
Other values (7) 1007
 
6.1%

Original Languages
Categorical

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Telugu
726 
Hindi
414 
Malayalam
355 
Bengali
151 
Marathi
148 

Length

Max length9
Median length8
Mean length6.6144958
Min length5

Characters and Unicode

Total characters12594
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTelugu
2nd rowTelugu
3rd rowTelugu
4th rowTelugu
5th rowTelugu

Common Values

ValueCountFrequency (%)
Telugu 726
38.1%
Hindi 414
21.7%
Malayalam 355
18.6%
Bengali 151
 
7.9%
Marathi 148
 
7.8%
Gujarati 110
 
5.8%

Length

2024-11-24T11:45:14.351149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-24T11:45:14.807488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
telugu 726
38.1%
hindi 414
21.7%
malayalam 355
18.6%
bengali 151
 
7.9%
marathi 148
 
7.8%
gujarati 110
 
5.8%

Most occurring characters

ValueCountFrequency (%)
a 2087
16.6%
l 1587
12.6%
u 1562
12.4%
i 1237
9.8%
e 877
7.0%
g 877
7.0%
T 726
 
5.8%
n 565
 
4.5%
M 503
 
4.0%
d 414
 
3.3%
Other values (9) 2159
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2087
16.6%
l 1587
12.6%
u 1562
12.4%
i 1237
9.8%
e 877
7.0%
g 877
7.0%
T 726
 
5.8%
n 565
 
4.5%
M 503
 
4.0%
d 414
 
3.3%
Other values (9) 2159
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2087
16.6%
l 1587
12.6%
u 1562
12.4%
i 1237
9.8%
e 877
7.0%
g 877
7.0%
T 726
 
5.8%
n 565
 
4.5%
M 503
 
4.0%
d 414
 
3.3%
Other values (9) 2159
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2087
16.6%
l 1587
12.6%
u 1562
12.4%
i 1237
9.8%
e 877
7.0%
g 877
7.0%
T 726
 
5.8%
n 565
 
4.5%
M 503
 
4.0%
d 414
 
3.3%
Other values (9) 2159
17.1%

Dubbed Languages
Categorical

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Hindi, Malayalam, Marathi, Gujarati, Bengali
726 
Telugu, Malayalam, Marathi, Gujarati, Bengali
414 
Telugu, Hindi, Marathi, Gujarati, Bengali
355 
Telugu, Hindi, Malayalam, Marathi, Gujarati
151 
Telugu, Malayalam, Bengali, Hindi
148 

Length

Max length45
Median length44
Mean length42.203782
Min length33

Characters and Unicode

Total characters80356
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHindi, Malayalam, Marathi, Gujarati, Bengali
2nd rowHindi, Malayalam, Marathi, Gujarati, Bengali
3rd rowHindi, Malayalam, Marathi, Gujarati, Bengali
4th rowHindi, Malayalam, Marathi, Gujarati, Bengali
5th rowHindi, Malayalam, Marathi, Gujarati, Bengali

Common Values

ValueCountFrequency (%)
Hindi, Malayalam, Marathi, Gujarati, Bengali 726
38.1%
Telugu, Malayalam, Marathi, Gujarati, Bengali 414
21.7%
Telugu, Hindi, Marathi, Gujarati, Bengali 355
18.6%
Telugu, Hindi, Malayalam, Marathi, Gujarati 151
 
7.9%
Telugu, Malayalam, Bengali, Hindi 148
 
7.8%
Telugu, Malayalam, Marathi, Bengali 110
 
5.8%

Length

2024-11-24T11:45:15.314277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-24T11:45:15.995512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
marathi 1756
19.0%
bengali 1753
18.9%
gujarati 1646
17.8%
malayalam 1549
16.7%
hindi 1380
14.9%
telugu 1178
12.7%

Most occurring characters

ValueCountFrequency (%)
a 14753
18.4%
i 7915
9.8%
, 7358
 
9.2%
7358
 
9.2%
l 6029
 
7.5%
u 4002
 
5.0%
r 3402
 
4.2%
t 3402
 
4.2%
M 3305
 
4.1%
n 3133
 
3.9%
Other values (11) 19699
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14753
18.4%
i 7915
9.8%
, 7358
 
9.2%
7358
 
9.2%
l 6029
 
7.5%
u 4002
 
5.0%
r 3402
 
4.2%
t 3402
 
4.2%
M 3305
 
4.1%
n 3133
 
3.9%
Other values (11) 19699
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14753
18.4%
i 7915
9.8%
, 7358
 
9.2%
7358
 
9.2%
l 6029
 
7.5%
u 4002
 
5.0%
r 3402
 
4.2%
t 3402
 
4.2%
M 3305
 
4.1%
n 3133
 
3.9%
Other values (11) 19699
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14753
18.4%
i 7915
9.8%
, 7358
 
9.2%
7358
 
9.2%
l 6029
 
7.5%
u 4002
 
5.0%
r 3402
 
4.2%
t 3402
 
4.2%
M 3305
 
4.1%
n 3133
 
3.9%
Other values (11) 19699
24.5%

Status
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Released
1904 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters15232
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 1904
100.0%

Length

2024-11-24T11:45:16.317578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-24T11:45:16.536385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
released 1904
100.0%

Most occurring characters

ValueCountFrequency (%)
e 5712
37.5%
R 1904
 
12.5%
l 1904
 
12.5%
a 1904
 
12.5%
s 1904
 
12.5%
d 1904
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5712
37.5%
R 1904
 
12.5%
l 1904
 
12.5%
a 1904
 
12.5%
s 1904
 
12.5%
d 1904
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5712
37.5%
R 1904
 
12.5%
l 1904
 
12.5%
a 1904
 
12.5%
s 1904
 
12.5%
d 1904
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5712
37.5%
R 1904
 
12.5%
l 1904
 
12.5%
a 1904
 
12.5%
s 1904
 
12.5%
d 1904
 
12.5%

India Malayalam Net
Real number (ℝ)

Zeros 

Distinct194
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0392794
Minimum0
Maximum130.25
Zeros1557
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:16.800846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.7685
Maximum130.25
Range130.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.3825604
Coefficient of variation (CV)6.1413325
Kurtosis147.11099
Mean1.0392794
Median Absolute Deviation (MAD)0
Skewness10.566558
Sum1978.788
Variance40.737078
MonotonicityNot monotonic
2024-11-24T11:45:17.144257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1557
81.8%
0.04 19
 
1.0%
0.02 14
 
0.7%
0.01 13
 
0.7%
0.03 13
 
0.7%
0.06 11
 
0.6%
0.1 10
 
0.5%
0.07 8
 
0.4%
0.15 8
 
0.4%
0.05 7
 
0.4%
Other values (184) 244
 
12.8%
ValueCountFrequency (%)
0 1557
81.8%
0.001 1
 
0.1%
0.002 1
 
0.1%
0.01 13
 
0.7%
0.02 14
 
0.7%
0.03 13
 
0.7%
0.04 19
 
1.0%
0.05 7
 
0.4%
0.06 11
 
0.6%
0.07 8
 
0.4%
ValueCountFrequency (%)
130.25 1
0.1%
85.16 1
0.1%
83.31 1
0.1%
77.58 1
0.1%
61 1
0.1%
58.56 1
0.1%
49.74 1
0.1%
47.9 1
0.1%
47.45 1
0.1%
44.83 1
0.1%

India Hindi Net
Real number (ℝ)

Zeros 

Distinct312
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2753566
Minimum0
Maximum1030.42
Zeros1501
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:17.905991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile35.3715
Maximum1030.42
Range1030.42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.417719
Coefficient of variation (CV)5.5554279
Kurtosis258.81086
Mean7.2753566
Median Absolute Deviation (MAD)0
Skewness13.190376
Sum13852.279
Variance1633.592
MonotonicityNot monotonic
2024-11-24T11:45:18.626956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1501
78.8%
0.01 14
 
0.7%
0.04 11
 
0.6%
0.07 9
 
0.5%
0.03 9
 
0.5%
0.1 8
 
0.4%
0.08 8
 
0.4%
0.02 7
 
0.4%
0.05 6
 
0.3%
0.3 3
 
0.2%
Other values (302) 328
 
17.2%
ValueCountFrequency (%)
0 1501
78.8%
0.01 14
 
0.7%
0.019 1
 
0.1%
0.02 7
 
0.4%
0.03 9
 
0.5%
0.04 11
 
0.6%
0.05 6
 
0.3%
0.06 3
 
0.2%
0.07 9
 
0.5%
0.08 8
 
0.4%
ValueCountFrequency (%)
1030.42 1
0.1%
597.99 1
0.1%
435.33 1
0.1%
342.57 1
0.1%
339.16 1
0.1%
302.15 1
0.1%
293.13 1
0.1%
272.78 1
0.1%
252.25 1
0.1%
247.37 1
0.1%

India Gujarati Net
Real number (ℝ)

Zeros 

Distinct73
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098943803
Minimum0
Maximum28.93
Zeros1797
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:19.295524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum28.93
Range28.93
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0981937
Coefficient of variation (CV)11.099166
Kurtosis364.43947
Mean0.098943803
Median Absolute Deviation (MAD)0
Skewness17.606452
Sum188.389
Variance1.2060295
MonotonicityNot monotonic
2024-11-24T11:45:20.437941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1797
94.4%
0.04 7
 
0.4%
0.02 6
 
0.3%
0.07 5
 
0.3%
0.03 5
 
0.3%
0.01 4
 
0.2%
0.05 3
 
0.2%
0.13 3
 
0.2%
0.09 3
 
0.2%
0.11 3
 
0.2%
Other values (63) 68
 
3.6%
ValueCountFrequency (%)
0 1797
94.4%
0.001 1
 
0.1%
0.004 1
 
0.1%
0.01 4
 
0.2%
0.018 1
 
0.1%
0.02 6
 
0.3%
0.026 1
 
0.1%
0.03 5
 
0.3%
0.04 7
 
0.4%
0.05 3
 
0.2%
ValueCountFrequency (%)
28.93 1
0.1%
18.7 1
0.1%
17.25 1
0.1%
16.59 1
0.1%
11.85 1
0.1%
10.85 1
0.1%
8.69 1
0.1%
7.53 1
0.1%
4.9 1
0.1%
4.47 1
0.1%

India Marathi Net
Real number (ℝ)

Zeros 

Distinct82
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25919118
Minimum0
Maximum76.28
Zeros1759
Zeros (%)92.4%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:21.254520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.09
Maximum76.28
Range76.28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7641954
Coefficient of variation (CV)10.664697
Kurtosis450.71932
Mean0.25919118
Median Absolute Deviation (MAD)0
Skewness19.314874
Sum493.5
Variance7.6407761
MonotonicityNot monotonic
2024-11-24T11:45:22.083464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1759
92.4%
0.04 9
 
0.5%
0.08 9
 
0.5%
0.09 8
 
0.4%
0.07 6
 
0.3%
0.12 6
 
0.3%
0.01 6
 
0.3%
0.03 5
 
0.3%
0.05 5
 
0.3%
0.19 4
 
0.2%
Other values (72) 87
 
4.6%
ValueCountFrequency (%)
0 1759
92.4%
0.01 6
 
0.3%
0.02 3
 
0.2%
0.03 5
 
0.3%
0.04 9
 
0.5%
0.05 5
 
0.3%
0.06 1
 
0.1%
0.07 6
 
0.3%
0.08 9
 
0.5%
0.09 8
 
0.4%
ValueCountFrequency (%)
76.28 1
0.1%
61.2 1
0.1%
37.72 1
0.1%
24.67 1
0.1%
23.55 1
0.1%
20.67 1
0.1%
16.13 1
0.1%
15.09 1
0.1%
14.12 1
0.1%
14.06 1
0.1%

India Bengali Net
Real number (ℝ)

Zeros 

Distinct76
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061324107
Minimum0
Maximum13.18
Zeros1774
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size15.0 KiB
2024-11-24T11:45:22.732884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.03
Maximum13.18
Range13.18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55994854
Coefficient of variation (CV)9.1309693
Kurtosis267.25526
Mean0.061324107
Median Absolute Deviation (MAD)0
Skewness14.764241
Sum116.7611
Variance0.31354236
MonotonicityNot monotonic
2024-11-24T11:45:23.119879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1774
93.2%
0.04 11
 
0.6%
0.01 10
 
0.5%
0.03 8
 
0.4%
0.02 6
 
0.3%
0.05 5
 
0.3%
0.1 4
 
0.2%
0.001 3
 
0.2%
0.06 3
 
0.2%
0.07 3
 
0.2%
Other values (66) 77
 
4.0%
ValueCountFrequency (%)
0 1774
93.2%
0.0001 2
 
0.1%
0.0003 1
 
0.1%
0.001 3
 
0.2%
0.003 1
 
0.1%
0.005 1
 
0.1%
0.006 2
 
0.1%
0.007 1
 
0.1%
0.01 10
 
0.5%
0.0166 1
 
0.1%
ValueCountFrequency (%)
13.18 1
0.1%
10.43 1
0.1%
8.22 1
0.1%
6.19 1
0.1%
6 1
0.1%
5.09 1
0.1%
4.25 1
0.1%
3.95 1
0.1%
3.7 1
0.1%
3.66 1
0.1%

Interactions

2024-11-24T11:45:00.440050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:34.357606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:38.523866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:40.816575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:43.229720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:45.621207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:48.005553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:51.429636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:55.552668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:57.934902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:00.693544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:34.951607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:38.764317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:41.058462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:43.480140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:45.851211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:48.383513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:51.795558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:55.795195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:58.182877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:00.904562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:35.529100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:38.962907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:41.282851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:43.697604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:46.075278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:48.744962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:52.162545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:56.026887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:58.450563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:01.146518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:36.090906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:39.205157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:41.524492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:43.959059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:46.314614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:49.110907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:52.480145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:56.281435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:58.700856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:01.392990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:36.642000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:39.446498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:41.771557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:44.193715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:46.551321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:49.399755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:52.888855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:56.528785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:58.946099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:01.626641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:37.007268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:39.656244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:42.004328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:44.427551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:46.771710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:49.728335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:53.145573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:56.761266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:59.184315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:01.879059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:37.404334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:39.885171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:42.267450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:44.657396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:47.017955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:50.074733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:54.610507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:56.989280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:59.430030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:02.099847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:37.776468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:40.100377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:42.501488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:44.885396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:47.244278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:50.416071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:54.819676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:57.220807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:59.662257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:02.349026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:38.015721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:40.339777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:42.734893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:45.137477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:47.480332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:50.746665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:55.050735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:57.455745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:59.900957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:02.589400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:38.283476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:40.571876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:42.996051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:45.389307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:47.723172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:51.109014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:55.311569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:44:57.712305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-24T11:45:00.160245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-24T11:45:23.868323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
BudgetDubbed LanguagesIndia Bengali NetIndia GrossIndia Gujarati NetIndia Hindi NetIndia Malayalam NetIndia Marathi NetIndia Telugu NetIndustryOriginal LanguagesOverseasVerdictWorldwide
Budget1.0000.000-0.1080.539-0.1120.268-0.006-0.0910.0880.0000.0000.4170.1040.540
Dubbed Languages0.0001.0000.1530.0450.1430.1060.1380.1300.0561.0001.0000.0000.1100.000
India Bengali Net-0.1080.1531.000-0.164-0.066-0.139-0.127-0.078-0.2020.1530.153-0.3040.047-0.173
India Gross0.5390.045-0.1641.000-0.1100.2940.034-0.0780.2100.0450.0450.8510.2340.989
India Gujarati Net-0.1120.143-0.066-0.1101.000-0.125-0.114-0.070-0.1820.1430.143-0.1830.091-0.116
India Hindi Net0.2680.106-0.1390.294-0.1251.000-0.241-0.147-0.3830.1060.1060.3340.2390.309
India Malayalam Net-0.0060.138-0.1270.034-0.114-0.2411.000-0.134-0.3500.1380.1380.0610.2000.032
India Marathi Net-0.0910.130-0.078-0.078-0.070-0.147-0.1341.000-0.2140.1300.130-0.2180.141-0.093
India Telugu Net0.0880.056-0.2020.210-0.182-0.383-0.350-0.2141.0000.0560.0560.1260.1680.209
Industry0.0001.0000.1530.0450.1430.1060.1380.1300.0561.0001.0000.0000.1100.000
Original Languages0.0001.0000.1530.0450.1430.1060.1380.1300.0561.0001.0000.0000.1100.000
Overseas0.4170.000-0.3040.851-0.1830.3340.061-0.2180.1260.0000.0001.0000.0650.925
Verdict0.1040.1100.0470.2340.0910.2390.2000.1410.1680.1100.1100.0651.0000.062
Worldwide0.5400.000-0.1730.989-0.1160.3090.032-0.0930.2090.0000.0000.9250.0621.000

Missing values

2024-11-24T11:45:02.973907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-24T11:45:03.807419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-24T11:45:04.408918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Released DateMovieWorldwideIndia Telugu NetIndia GrossOverseasBudgetVerdictIndustryOriginal LanguagesDubbed LanguagesStatusIndia Malayalam NetIndia Hindi NetIndia Gujarati NetIndia Marathi NetIndia Bengali Net
025 DecSolo Brathuke So Better22.3018.1021.101.2020.0HitTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
113 MarShivan0.250.200.230.022.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
213 MarEureka0.350.250.300.053.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
313 MarMadha0.500.350.420.083.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
413 MarArjuna0.350.250.300.053.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
513 Mar3020.070.050.070.002.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
606 MarCollege Kumar0.470.160.470.001.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
706 MarPalasa 19783.202.502.950.255.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
806 MarO Pitta Katha0.900.770.900.005.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
906 MarAnukunnadi Okati Ayindi Okkati0.110.090.110.005.0UnknownTollywoodTeluguHindi, Malayalam, Marathi, Gujarati, BengaliReleased0.00.00.00.00.0
Released DateMovieWorldwideIndia Telugu NetIndia GrossOverseasBudgetVerdictIndustryOriginal LanguagesDubbed LanguagesStatusIndia Malayalam NetIndia Hindi NetIndia Gujarati NetIndia Marathi NetIndia Bengali Net
189423 FebAhalya (Bengali)0.110.00.11NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.09
189509 FebBhootpori0.680.00.68NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.62
189609 FebSedin Kuyasha Chilo0.080.00.08NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.07
189709 FebPariah0.500.00.50NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.46
189819 JanNeelkuthir Rahashya0.000.00.00NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.00
189919 JanAsha0.000.00.00NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.00
190019 JanSentimentaaal0.120.00.12NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.11
190119 JanHubba0.360.00.36NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.31
190212 JanBijoyar Pore0.280.00.28NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.27
190312 JanShri Swapankumarer Badami Hyenar Kobole0.530.00.53NaN1.0UnknownBengaliBengaliTelugu, Hindi, Malayalam, Marathi, GujaratiReleased0.00.00.00.00.48